OpenRaiser/Intern-Atlas huggingface dataset:
Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges
Venue: arXiv cs.IT 2023
Year: 2023

Modern cellular networks are multi-cell and use universal frequency reuse to maximize spectral efficiency. This results in high inter-cell interference. This problem is growing as cellular networks become three-dimensional with the adoption of unmanned aerial vehicles (UAVs). This is because the strength and number of interference links rapidly increase due to the line-of-sight channels in UAV communications. Existing interference management solutions need each transmitter to know the channel information of interfering signals, rendering them impractical due to excessive signaling overhead. In this paper, we propose leveraging deep reinforcement learning for interference management to tackle this shortcoming. In particular, we show that interference can still be effectively mitigated even without knowing its channel information. We then discuss novel approaches to scale the algorithms with linear/sublinear complexity and decentralize them using multi-agent reinforcement learning. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
Commitment over Gaussian Unfair Noisy Channels
Venue: arXiv cs.IT 2023
Year: 2023

Commitment is a key primitive which resides at the heart of several cryptographic protocols. Noisy channels can help realize information-theoretically secure commitment schemes, however, their imprecise statistical characterization can severely impair such schemes, especially their security guarantees. Keeping our focus on channel unreliability in this work, we study commitment over unreliable continuous alphabet channels called the Gaussian unfair noisy channels or Gaussian UNCs. We present the first results on the optimal throughput or commitment capacity of Gaussian UNCs. It is known that classical Gaussian channels have infinite commitment capacity, even under finite transmit power constraints. For unreliable Gaussian UNCs, we prove the surprising result that their commitment capacity may be finite, and in some cases, zero. When commitment is possible, we present achievable rate lower bounds by constructing positive - throughput protocols under given input power constraint, and (two-sided) channel elasticity at committer Alice and receiver Bob. Our achievability results establish an interesting fact - Gaussian UNCs with zero elasticity have infinite commitment capacity - which brings a completely new perspective to why classic Gaussian channels, i.e., Gaussian UNCs with zero elasticity, have infinite capacity. Finally, we precisely characterize the positive commitment capacity threshold for a Gaussian UNC in terms of the channel elasticity, when the transmit power tends to infinity.
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
Venue: arXiv cs.SD 2023
Year: 2023

This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. This is extended to present an inherently interpretable model with high performance. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In particular, an interpreter is trained to generate a regularized intermediate embedding from hidden layers of a target network, learnt as time-activations of a pre-learnt NMF dictionary. Our methodology allows us to generate intuitive audio-based interpretations that explicitly enhance parts of the input signal most relevant for a network's decision. We demonstrate our method's applicability on a variety of classification tasks, including multi-label data for real-world audio and music.
Fast Pareto Optimization Using Sliding Window Selection
Venue: arXiv cs.NE 2023
Year: 2023

Pareto optimization using evolutionary multi-objective algorithms has been widely applied to solve constrained submodular optimization problems. A crucial factor determining the runtime of the used evolutionary algorithms to obtain good approximations is the population size of the algorithms which grows with the number of trade-offs that the algorithms encounter. In this paper, we introduce a sliding window speed up technique for recently introduced algorithms. We prove that our technique eliminates the population size as a crucial factor negatively impacting the runtime and achieves the same theoretical performance guarantees as previous approaches within less computation time. Our experimental investigations for the classical maximum coverage problem confirms that our sliding window technique clearly leads to better results for a wide range of instances and constraint settings.
Learning to Code on Graphs for Topological Interference Management
Venue: arXiv cs.IT 2023
Year: 2023

The state-of-the-art coding schemes for topological interference management (TIM) problems are usually handcrafted for specific families of network topologies, relying critically on experts' domain knowledge. This inevitably restricts the potential wider applications to wireless communication systems, due to the limited generalizability. This work makes the first attempt to advocate a novel intelligent coding approach to mimic topological interference alignment (IA) via local graph coloring algorithms, leveraging the new advances of graph neural networks (GNNs) and reinforcement learning (RL). The proposed LCG framework is then generalized to discover new IA coding schemes, including one-to-one vector IA and subspace IA. The extensive experiments demonstrate the excellent generalizability and transferability of the proposed approach, where the parameterized GNNs trained by small size TIM instances are able to work well on new unseen network topologies with larger size.
Gotcha! I Know What You are Doing on the FPGA Cloud: Fingerprinting Co-Located Cloud FPGA Accelerators via Measuring Communication Links
Venue: arXiv cs.CR 2023
Year: 2023

In recent decades, due to the emerging requirements of computation acceleration, cloud FPGAs have become popular in public clouds. Major cloud service providers, e.g. AWS and Microsoft Azure have provided FPGA computing resources in their infrastructure and have enabled users to design and deploy their own accelerators on these FPGAs. Multi-tenancy FPGAs, where multiple users can share the same FPGA fabric with certain types of isolation to improve resource efficiency, have already been proved feasible. However, this also raises security concerns. Various types of side-channel attacks targeting multi-tenancy FPGAs have been proposed and validated. The awareness of security vulnerabilities in the cloud has motivated cloud providers to take action to enhance the security of their cloud environments. In FPGA security research papers, researchers always perform attacks under the assumption that attackers successfully co-locate with victims and are aware of the existence of victims on the same FPGA board. However, the way to reach this point, i.e., how attackers secretly obtain information regarding accelerators on the same fabric, is constantly ignored despite the fact that it is non-trivial and important for attackers. In this paper, we present a novel fingerprinting attack to gain the types of co-located FPGA accelerators. We utilize a seemingly non-malicious benchmark accelerator to sniff the communication link and collect performance traces of the FPGA-host communication link. By analyzing these traces, we are able to achieve high classification accuracy for fingerprinting co-located accelerators, which proves that attackers can use our method to perform cloud FPGA accelerator fingerprinting with a high success rate. As far as we know, this is the first paper targeting multi-tenant FPGA accelerator fingerprinting with the communication side-channel.
Rethinking k-means from manifold learning perspective
Venue: arXiv cs.LG 2023
Year: 2023

Although numerous clustering algorithms have been developed, many existing methods still leverage k-means technique to detect clusters of data points. However, the performance of k-means heavily depends on the estimation of centers of clusters, which is very difficult to achieve an optimal solution. Another major drawback is that it is sensitive to noise and outlier data. In this paper, from manifold learning perspective, we rethink k-means and present a new clustering algorithm which directly detects clusters of data without mean estimation. Specifically, we construct distance matrix between data points by Butterworth filter such that distance between any two data points in the same clusters equals to a small constant, while increasing the distance between other data pairs from different clusters. To well exploit the complementary information embedded in different views, we leverage the tensor Schatten p-norm regularization on the 3rd-order tensor which consists of indicator matrices of different views. Finally, an efficient alternating algorithm is derived to optimize our model. The constructed sequence was proved to converge to the stationary KKT point. Extensive experimental results indicate the superiority of our proposed method.
Age-Energy Trade-off in Status Update System with Wake-up Control
Venue: arXiv cs.IT 2023
Year: 2023

In the status update system, the freshness of information is drawing more and more attention. To measure the freshness of the data, age-of-synchronization (AoS) is introduced. Since many communication devices are energy-constrained, how to reduce energy consumption while keeping the timely response of data needs to be carefully addressed. In this paper, we study the age-energy trade-off problem in a single-source single-server scenario. We assume the server enters a low-power sleep state when idle to save energy and consider three wake-up policies. We adopt the stochastic hybrid system (SHS) method to analyze the average AoS and average energy consumption under different policies. The age-energy trade-off relationship under different parameters is illustrated by numerical results.
Transavs: End-To-End Audio-Visual Segmentation With Transformer
Venue: arXiv cs.SD 2023
Year: 2023

Audio-Visual Segmentation (AVS) is a challenging task, which aims to segment sounding objects in video frames by exploring audio signals. Generally AVS faces two key challenges: (1) Audio signals inherently exhibit a high degree of information density, as sounds produced by multiple objects are entangled within the same audio stream; (2) Objects of the same category tend to produce similar audio signals, making it difficult to distinguish between them and thus leading to unclear segmentation results. Toward this end, we propose TransAVS, the first Transformer-based end-to-end framework for AVS task. Specifically, TransAVS disentangles the audio stream as audio queries, which will interact with images and decode into segmentation masks with full transformer architectures. This scheme not only promotes comprehensive audio-image communication but also explicitly excavates instance cues encapsulated in the scene. Meanwhile, to encourage these audio queries to capture distinctive sounding objects instead of degrading to be homogeneous, we devise two self-supervised loss functions at both query and mask levels, allowing the model to capture distinctive features within similar audio data and achieve more precise segmentation. Our experiments demonstrate that TransAVS achieves state-of-the-art results on the AVSBench dataset, highlighting its effectiveness in bridging the gap between audio and visual modalities.
Minimum Consistent Subset for Trees Revisited
Venue: arXiv cs.DS 2023
Year: 2023

In a vertex-colored graph $G = (V, E)$, a subset $S \subseteq V$ is said to be consistent if every vertex has a nearest neighbor in $S$ with the same color. The problem of computing a minimum cardinality consistent subset of a graph is known to be NP-hard. On the positive side, Dey et al. (FCT 2021) show that this problem is solvable in polynomial time when input graphs are restricted to bi-colored trees. In this paper, we give a polynomial-time algorithm for this problem on $k$-colored trees with fixed $k$.
Reconfiguration of Time-Respecting Arborescences
Venue: arXiv cs.DS 2023
Year: 2023

An arborescence, which is a directed analogue of a spanning tree in an undirected graph, is one of the most fundamental combinatorial objects in a digraph. In this paper, we study arborescences in digraphs from the viewpoint of combinatorial reconfiguration, which is the field where we study reachability between two configurations of some combinatorial objects via some specified operations. Especially, we consider reconfiguration problems for time-respecting arborescences, which were introduced by Kempe, Kleinberg, and Kumar. We first prove that if the roots of the initial and target time-respecting arborescences are the same, then the target arborescence is always reachable from the initial one and we can find a shortest reconfiguration sequence in polynomial time. Furthermore, we show if the roots are not the same, then the target arborescence may not be reachable from the initial one. On the other hand, we show that we can determine whether the target arborescence is reachable form the initial one in polynomial time. Finally, we prove that it is NP-hard to find a shortest reconfiguration sequence in the case where the roots are not the same. Our results show an interesting contrast to the previous results for (ordinary) arborescences reconfiguration problems.
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Venue: arXiv cs.CV 2023
Year: 2023

Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NAS only consider $l_{\infty}$ norm-based adversarial noises. In order to improve the robustness of DNN models against multiple types of noises, it is necessary to consider a comprehensive evaluation in NAS for robust architectures. But with the increasing number of types of robustness evaluations, it also becomes more time-consuming to find comprehensively robust architectures. To alleviate this problem, we propose a novel efficient search of comprehensively robust neural architectures via multi-fidelity evaluation (ES-CRNA-ME). Specifically, we first search for comprehensively robust architectures under multiple types of evaluations using the weight-sharing-based NAS method, including different $l_{p}$ norm attacks, semantic adversarial attacks, and composite adversarial attacks. In addition, we reduce the number of robustness evaluations by the correlation analysis, which can incorporate similar evaluations and decrease the evaluation cost. Finally, we propose a multi-fidelity online surrogate during optimization to further decrease the search cost. On the basis of the surrogate constructed by low-fidelity data, the online high-fidelity data is utilized to finetune the surrogate. Experiments on CIFAR10 and CIFAR100 datasets show the effectiveness of our proposed method.
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Venue: arXiv cs.IT 2023
Year: 2023

Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in challenging communication scenarios. However, wireless receiver design poses unique challenges that fundamentally differ from those encountered in traditional deep learning domains. The main challenges arise from the limited power and computational resources of wireless devices, as well as from the dynamic nature of wireless communications, which causes continual changes to the data distribution. These challenges impair conventional AI based on highly-parameterized DNNs, motivating the development of adaptive, flexible, and light-weight AI for wireless communications, which is the focus of this article. Here, we propose that AI-based design of wireless receivers requires rethinking of the three main pillars of AI: architecture, data, and training algorithms. In terms of architecture, we review how to design compact DNNs via model-based deep learning. Then, we discuss how to acquire training data for deep receivers without compromising spectral efficiency. Finally, we review efficient, reliable, and robust training algorithms via meta-learning and generalized Bayesian learning. Numerical results are presented to demonstrate the complementary effectiveness of each of the surveyed methods. We conclude by presenting opportunities for future research on the development of practical deep receivers
Echoes: a 200 GOPS/W Frequency Domain SoC with FFT Processor and I2S DSP for Flexible Data Acquisition from Microphone Arrays
Venue: arXiv cs.AR 2023
Year: 2023

Emerging applications in the IoT domain require ultra-low-power and high-performance end-nodes to deal with complex near-sensor-data analytics. Domains such as audio, radar, and Structural Health Monitoring require many computations to be performed in the frequency domain rather than in the time domain. We present ECHOES, a System-On-a-Chip (SoC) composed of a RISC-V core enhanced with fixed and floating-point digital signal processing (DSP) extensions and a Fast-Fourier Transform (FFT) hardware accelerator targeting emerging frequency-domain application. The proposed SoC features an autonomous I/O engine supporting a wide set of peripherals, including Ultra-Low-Power radars, MEMS, and digital microphones over I2S protocol with full-duplex Time Division Multiplexing DSP mode, making ECHOES the first open-source SoC which offers this functionality enabling simultaneous communication with up to 16 I/Os devices. ECHOES, fabricated with 65nm CMOS technology, reaches a peak performance of 0.16 GFLOPS and a peak energy efficiency of 9.68 GFLOPS/W on a wide range of floating and fixed-point general-purpose DSP kernels. The FFT accelerator achieves performance up to 10.16 GOPS with an efficiency of 199.8 GOPS/W, improving performance and efficiency by up to 41.1x and 11.2x, respectively, over its software implementation of this critical task for frequency domain processing.
QoT estimation using EGN-assisted machine learning for multi-period network planning
Venue: arXiv cs.NI 2023
Year: 2023

The rapidly growing traffic demands in fiber-optical networks require flexibility and accuracy in configuring lightpaths, for which fast and accurate quality of transmission (QoT) estimation is of pivotal importance. This paper introduces a machine learning (ML)-based QoT estimation approach that meets these requirements. The proposed gradient-boosting ML model uses precomputed per-channel self-channel-interference values as representative and condensed features to estimate non-linear interference in a flexible-grid network. With an enhanced Gaussian noise (GN) model simulation as the baseline, the ML model achieves a mean absolute signal-to-noise ratio error of approximately 0.1 dB, which is an improvement over the GN model. For three different network topologies and network planning approaches of varying complexities, a multi-period network planning study is performed in which ML and GN are compared as path computation elements (PCEs). The results show that the ML PCE is capable of matching or slightly improving the performance of the GN PCE on all topologies while reducing significantly the computation time of network planning by up to 70%.
Methods and Tools to Advance the Retrieval of Mathematical Knowledge from Digital Libraries for Search-, Recommendation-, and Assistance-Systems
Venue: arXiv cs.IR 2023
Year: 2023

This project investigated new approaches and technologies to enhance the accessibility of mathematical content and its semantic information for a broad range of information retrieval applications. To achieve this goal, the project addressed three main research challenges: (1) syntactic analysis of mathematical expressions, (2) semantic enrichment of mathematical expressions, and (3) evaluation using quality metrics and demonstrators. To make our research useful for the research community, we published tools that enable researchers to process mathematical expressions more effectively and efficiently.
BundleRecon: Ray Bundle-Based 3D Neural Reconstruction
Venue: arXiv cs.CV 2023
Year: 2023

With the growing popularity of neural rendering, there has been an increasing number of neural implicit multi-view reconstruction methods. While many models have been enhanced in terms of positional encoding, sampling, rendering, and other aspects to improve the reconstruction quality, current methods do not fully leverage the information among neighboring pixels during the reconstruction process. To address this issue, we propose an enhanced model called BundleRecon. In the existing approaches, sampling is performed by a single ray that corresponds to a single pixel. In contrast, our model samples a patch of pixels using a bundle of rays, which incorporates information from neighboring pixels. Furthermore, we design bundle-based constraints to further improve the reconstruction quality. Experimental results demonstrate that BundleRecon is compatible with the existing neural implicit multi-view reconstruction methods and can improve their reconstruction quality.
Do RESTful API Design Rules Have an Impact on the Understandability of Web APIs? A Web-Based Experiment with API Descriptions
Venue: arXiv cs.SE 2023
Year: 2023

Context: Web APIs are one of the most used ways to expose application functionality on the Web, and their understandability is important for efficiently using the provided resources. While many API design rules exist, empirical evidence for the effectiveness of most rules is lacking. Objective: We therefore wanted to study 1) the impact of RESTful API design rules on understandability, 2) if rule violations are also perceived as more difficult to understand, and 3) if demographic attributes like REST-related experience have an influence on this. Method: We conducted a controlled Web-based experiment with 105 participants, from both industry and academia and with different levels of experience. Based on a hybrid between a crossover and a between-subjects design, we studied 12 design rules using API snippets in two complementary versions: one that adhered to a "rule" and one that was a "violation" of this rule. Participants answered comprehension questions and rated the perceived difficulty. Results: For 11 of the 12 rules, we found that "violation" performed significantly worse than "rule" for the comprehension tasks. Regarding the subjective ratings, we found significant differences for 9 of the 12 rules, meaning that most violations were subjectively rated as more difficult to understand. Demographics played no role in the comprehension performance for "violation". Conclusions: Our results provide first empirical evidence for the importance of following design rules to improve the understandability of Web APIs, which is important for researchers, practitioners, and educators.